Estimation and Testing Under Sparsity: École d'Été de Probabilités de Saint-Flour XLV - 2015 (Lecture Notes in Mathematics)
暫譯: 稀疏下的估計與檢驗：聖佛朗索夏季學院 XLV - 2015（數學講義）

Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm. The illustrations provided include generalized linear models, density estimation, matrix completion and sparse principal components. Each chapter ends with a problem section. The book can be used as a textbook for a graduate or PhD course.